1. Field of the Invention
The present invention relates to systems and methods of measuring, estimating, or otherwise determining the state-of-age of a battery, and more particularly to an improved method of estimating state-of-age including the steps of storing a data bucket of instantaneous charge or discharge points, averaging the points, determining a parameter based on a rate of change in the averaged points, and comparing the parameter to a scalar or database.
2. Discussion of Prior Art
It is often desirous to determine the state-of-age (also referred to herein as “age”), so as to predict the remaining life (i.e., the remaining period of viability) of a battery in use; and in certain applications, such as vital sign monitoring equipment or remote vehicle operation it can be critical. With respect to vehicular use, for example, the battery has been described as the heart of the electrical system and typically presents a conventional, lead-acid, AGM, or hybrid secondary (i.e., rechargeable) cell configuration.
It is well appreciated by those of ordinary skill in the art that where a battery has transcended its useful life, it ceases to be able to hold a charge despite connection to a fully functioning charging system. This makes the essentiality of providing accurate and reliable means for determining battery state-of-age readily apparent. Unfortunately, determining battery state-of-age is not simply a measure of its manufacture date. For example, it is also appreciated that battery age is dependant upon the level of daily discharge experienced. That is to say, a battery undergoing an average daily discharge of 10 percent may have a life that is five times as long as one discharged daily at 50 percent.
Conventionally battery inquiries regarding age or viability formerly involved a technician using a voltmeter to perform a multitude of tests, including a state-of-charge and/or battery capacity load test. More recently means of estimating battery age (also known as state-of-life or state-of-health (SOH)) have been included within a battery state estimator (“BSE”) module. Controllers programmably equipped with these modules are configured to sample sensor data at high rates, and subsequently extract battery parameters by utilizing model regression/fitting techniques, or taking known endpoints and assumed/interpolated trends between age endpoints. In addition to battery age, these modules constantly determine other conditions, including whether and to what extent the battery is recharging. Finally, the parameters may be requested by and/or autonomously determined so as to provide information to another node within a communication network or an operator.
It is also appreciated by those of ordinary skill in the art that one extractable parameter, “high frequency” (e.g., internal 100-millisecond) resistance, exhibits a strong, distinct trend with and therefore may be used to determine battery age. More particularly, from the value of the regressed resistance and other known quantities, such as for example in a vehicular application, the power requirement to crank the engine, the interested node or operator can discern the age of the battery and therefore predict the remaining life. It is further appreciated that for all battery chemistries seen to date, the higher the frequency the stronger the trend is with respect to increasing battery age. Therefore, in conventional BSE methodology, the highest possible frequency resistance (DC resistance) has been elicited from the battery under the most instantaneous demands (i.e. high current/power demands over very short periods), which typically occur when a vehicle is cranking for example.
A concern arising when using conventional BSE modules, however, is that they typically present all-or-nothing functionality. That is to say, their current algorithms involve more data retrieval and computation than is necessary to extract only the data needed to determine battery age for example. Of further concern, these modules are also configured to manipulate input signals in order to improve reliability, making them overly complex for applications requiring only battery age information. Thus, it follows that these modules present inefficient means of determining only one parameter, such as battery age, which results in increased energy consumption, communication load and other operational/networking costs.
Another prior art method of performing autonomous battery life prediction involves a direct measurement technique utilizing electrochemical impedance spectroscopy (“EIS”). This method requires imposing a small input signal to the battery at a predefined frequency, and enables accurate impedance response measuring. Concernedly, however, this technique requires that the measurement device remains stable during the measurement process, so that the sensory cables remain free of induced electromagnetic noise. This is especially concerning in a vehicular setting, due to an abundance of uneven and poorly repaired roadways, as well as unavoidable driver caused eccentricities. Moreover, in this configuration, the sampled battery must be disconnected from parallel impedances during measurement. Of yet further concern regarding EIS systems, is the requirement of additional hardware to induce the necessary waveform. For these reasons and more, EIS systems have not been readily implemented and as such have received low market penetration.
As societal dependency upon electric power increases, so too does the need for reliable battery life prediction. This is especially the case when it comes to vehicular use, where there remains a need in the art for a more efficient yet accurate method of determining battery state-of-age.